Nucleic acids encoding mosaic HIV-1 gag proteins

ABSTRACT

The disclosure generally relates to an immunogenic composition (e.g., a vaccine) and, in particular, to a polyvalent immunogenic composition, such as a polyvalent HIV vaccine, and to methods of using same.

This application is a divisional of U.S. application Ser. No. 13/399,963, filed Feb. 17, 2012, now U.S. Pat. No. 9,011,875, which is a continuation of U.S. application Ser. No. 11/990,222, filed Apr. 20 2009, now U.S. Pat. No. 8,119,140, which is the U.S. national phase of International Application No. PCT/US2006/032907, filed Aug. 23, 2006, which designated the U.S. and claims the benefit of priority from U.S. Provisional Application No. 60/710,154, filed Aug. 23, 2005, and 60/739,413, filed Nov. 25, 2005, the entire contents of each of which are hereby incorporated by reference.

This invention was made with Government support under Contract No. DE-AC52-06NA25396 awarded by the U.S. Department of Energy. The Government has certain rights in the invention.

SEQUENCE

The instant application contains a “lengthy” Sequence Listing which has been submitted as an ASCII text file via EFS-Web in lieu of a paper copy, and is hereby incorporated by reference in its entirety. The ASCIII text file, recorded on May 18, 2012, contains a copy of an 870 KB file named Sequence_Listing.TXT.

TECHNICAL FIELD

The present invention relates, in general, to an immunogenic composition (e.g., a vaccine) and, in particular, to a polyvalent immunogenic composition, such as a polyvalent HIV vaccine, and to methods of using same. The invention further relates to methods that use a genetic algorithm to create sets of polyvalent antigens suitable for use, for example, in vaccination strategies.

BACKGROUND

Designing an effective HIV vaccine is a many-faceted challenge. The vaccine preferably elicits an immune response capable of either preventing infection or, minimally, controlling viral replication if infection occurs, despite the failure of immune responses to natural infection to eliminate the virus (Nabel, Vaccine 20:1945-1947 (2002)) or to protect from superinfection (Altfeld et al, Nature 420:434-439 (2002)). Potent vaccines are needed, with optimized vectors, immunization protocols, and adjuvants (Nabel, Vaccine 20:1945-1947 (2002)), combined with antigens that can stimulate cross-reactive responses against the diverse spectrum of circulating viruses (Gaschen et al, Science 296:2354-2360 (2002), Korber et al, Br. Med. Bull. 58:19-42 (2001)). The problems that influenza vaccinologists have confronted for decades highlight the challenge posed by HIV-1: human influenza strains undergoing antigenic drift diverge from one another by around 1-2% per year, yet vaccine antigens often fail to elicit cross-reactive B-cell responses from one year to the next, requiring that contemporary strains be continuously monitored and vaccines be updated every few years (Korber et al, Br. Med. Bull. 58:19-42 (2001)). In contrast, co-circulating individual HIV-1 strains can differ from one another by 20% or more in relatively conserved proteins, and up to 35% in the Envelope protein (Gaschen et al, Science 296:2354-2360 (2002), Korber et al, Br. Med. Bull. 58:19-42 (2001)).

Different degrees of viral diversity in regional HIV-1 epidemics provide a potentially useful hierarchy for vaccine design strategies. Some geographic regions recapitulate global diversity, with a majority of known HIV-1 subtypes, or clades, co-circulating (e.g., the Derrocratic Republic of the Congo (Mokili & Korber, J. Neurovirol 11(Suppl. 1):66-75 (2005)); others are dominated by two subtypes and their recombinants (e.g., Uganda (Barugahare et al, J. Virol. 79:4132-4139 (2005)), and others by a single subtype (e.g., South Africa (Williamson et al, AIDS Res. Hum. Retroviruses 19:133-144 (2003)). Even areas with predominantly single-subtype epidemics must address extensive within-clade diversity (Williamson et al, AIDS Res. Hum. Retroviruses 19:133-44 (2003)) but, since international travel can be expected to further blur geographic distinctions, all nations would benefit from a global vaccine.

Presented herein is the design of polyvalent vaccine antigen sets focusing on T lymphocyte responses, optimized for either the common B and C subtypes, or all HIV-1 variants in global circulation [the HIV-1 Main (M) group]. Cytotoxic T-lymphocytes (CTL) directly kill infected, virus-producing host cells, recognizing them via viral protein fragments (epitopes) presented on infected cell surfaces by human leukocyte antigen (HLA) molecules. Helper T-cell responses control varied aspects of the immune response through the release of cytokines. Both are likely to be crucial for an HIV-1 vaccine: CTL responses have been implicated in slowing disease progression (Oxenius et al, J. Infect. Dis. 189:1199-208 (2004)); vaccine-elicited cellular immune responses in nonhuman primates help control pathogenic SW or SHIV, reducing the likelihood of disease after challenge (Barouch et al, Science 290:486-92 (2000)); and experimental depletion of CD8+ T-cells results in increased viremia in SW infected rhesus macaques Schmitz et al, Science 283:857-60 (1999)). Furthermore, CTL escape mutations are associated with disease progression (Barouch et al, J. Virol. 77:7367-75 (2003)), thus vaccine-stimulated memory responses that block potential escape routes may be valuable.

The highly variable Env protein is the primary target for neutralizing antibodies against HIV; since immune protection will likely require both B-cell and T-cell responses (Moore and Burton, Nat. Med. 10:769-71 (2004)), Env vaccine antigens will also need to be optimized separately to elicit antibody responses. T-cell-directed vaccine components, in contrast, can target the more conserved proteins, but even the most conserved HIV-1 proteins are diverse enough that variation is an issue. Artificial central-sequence vaccine approaches (e.g., consensus sequences, in which every amino acid is found in a plurality of sequences, or maximum likelihood reconstructions of ancestral sequences (Gaschen et al, Science 296:2354-60 (2002), Gao et al, J. Virol. 79:1154-63 (2005), Doria-Rose et al, J. Virol. 79:11214-24 (2005), Weaver et al, J. Virol., in press)) are promising; nevertheless, even centralized strains provide limited coverage of HIV-1 variants, and consensus-based reagents fail to detect many autologous T-cell responses (Altfeld et al, J. Virol. 77:7330-40 (2003)).

Single amino acid changes can allow an epitope to escape T-cell surveillance; since many T-cell epitopes differ between HIV-1 strains at one or more positions, potential responses to any single vaccine antigen are limited. Whether a particular mutation results in escape depends upon the specific epitope/T-cell combination, although some changes broadly affect between-subtype cross-reactivity (Norris et al, AIDS Res. Hum. Retroviruses 20:315-25 (2004)). Including multiple variants in a polyvalent vaccine could enable responses to a broader range of circulating variants, and could also prime the immune system against common escape mutants (Jones et al, J. Exp. Med. 200:1243-56 (2004)). Escape from one T-cell receptor may create a variant that is susceptible to another (Allen et al, J. Virol. 79:12952-60 (2005), Feeney et al, J. Immunol. 174:7524-30 (2005)), so stimulating polyclonal responses to epitope variants may be beneficial (Killian et al, Aids 19:887-96 (2005)). Escape mutations that inhibit processing (Milicic et al, J. Immunol. 175:4618-26 (2005)) or HLA binding (Ammaranond et al, AIDS Res. Hum. Retroviruses 21:395-7 (2005)) cannot be directly countered by a T-cell with a different specificity, but responses to overlapping epitopes may block even some of these escape routes.

The present invention relates to a polyvalent vaccine comprising several “mosaic” proteins (or genes encoding these proteins). The candidate vaccine antigens can be cocktails of k composite proteins (k being the number of sequence variants in the cocktail), optimized to include the maximum number of potential T-cell epitopes in an input set of viral proteins. The mosaics are generated from natural sequences: they resemble natural proteins and include the most common forms of potential epitopes. Since CD8+ epitopes are contiguous and typically nine amino-acids long, sets of mosaics can be scored by “coverage” of nonamers (9-mers) in the natural sequences (fragments of similar lengths are also well represented). 9-Mers not found at least three times can be excluded. This strategy provides the level of diversity coverage achieved by a massively polyvalent multiple-peptide vaccine but with important advantages: it allows vaccine delivery as intact proteins or genes, excludes low-frequency or unnatural epitopes that are not relevant to circulating strains, and its intact protein antigens are more likely to be processed as in a natural infection.

SUMMARY OF THE INVENTION

In general, the present invention relates to an immunogenic composition. More specifically, the invention relates to a polyvalent immunogenic composition (e.g., an HIV vaccine), and to methods of using same. The invention further relates to methods that involve the use of a genetic algorithm to design sets of polyvalent antigens suitable for use as vaccines.

Objects and advantages of the present invention will be clear from the description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F. The upper bound of potential epitope coverage of the HIV-1 M group. The upper bound for population coverage of 9-mers for increasing numbers of variants is shown, for k=1-8 variants. A sliding window of length nine was applied across aligned sequences, moving down by one position. Different colors denote results for different numbers of sequences. At each window, the coverage given by the k most common 9-mers is plotted for Gag (FIGS. 1A and 1B), Nef (FIGS. 1C and 1D) and Env gp120 (FIGS. 1E and 1F). Gaps inserted to maintain the alignment are treated as characters. The diminishing returns of adding more variants are evident, since, as k increases, increasingly rare forms are added. In FIGS. 1A, 1C and 1E, the scores for each consecutive 9-mer are plotted in their natural order to show how diversity varies in different protein regions; both p24 in the center of Gag and the central region of Nef are particularly highly conserved. In FIGS. 1B, ID and IF, the scores for each 9-mer are reordered by coverage (a strategy also used in FIG. 4), to provide a sense of the overall population coverage of a given protein. Coverage of gp120, even with 8 variant 9-mers, is particularly poor (FIGS. 1E and 1F).

FIGS. 2A-2C. Mosaic initialization, scoring, and optimization. FIG. 2A) A set of k populations is generated by random 2-point recombination of natural sequences (1-6 populations of 50-500 sequences each have been tested). One sequence from each population is chosen (initially at random) for the mosaic cocktail, which is subsequently optimized. The cocktail sequences are scored by computing coverage (defined as the mean fraction of natural-sequence 9-mers included in the cocktail, averaged over all natural sequences in the input data set). Any new sequence that covers more epitopes will increase the score of the whole cocktail. FIG. 2B) The fitness score of any individual sequence is the coverage of a cocktail containing that sequence plus the current representatives from other populations. FIG. 2C) Optimization: 1) two “parents” are chosen: the higher-scoring of a randomly chosen pair of recombined sequences, and either (with 50% probability) the higher-scoring sequence of a second random pair, or a randomly chosen natural sequence. 2) Two-point recombination between the two parents is used to generate a “child” sequence. If the child contains unnatural or rare 9-mers, it is immediately rejected, otherwise it is scored (Gaschen et al, Science 296:2354-2360 (2002)). If the score is higher than that of any of four randomly-selected population members, the child is inserted in the population in place of the weakest of the four, thus evolving an improved population; 4) if its score is a new high score, the new child replaces the current cocktail member from its population. Ten cycles of child generation are repeated for each population in turn, and the process iterates until improvement stalls.

FIG. 3. Mosaic strain coverage for all HIV proteins. The level of 9-mer coverage achieved by sets of four mosaic proteins for each HIV protein is shown, with mosaics optimized using either the M group or the C subtype. The fraction of C subtype sequence 9-mers covered by mosaics optimized on the C subtype (within-clade optimization) is shown in gray. Coverage of 9-mers found in non-C subtype M-group sequences by subtype-C-optimized mosaics (between-clade coverage) is shown in white. Coverage of subtype C sequences by M-group optimized mosaics is shown in black. B clade comparisons gave comparable results (data not shown).

FIGS. 4A-4F. Coverage of M group sequences by different vaccine candidates, nine-mer by nine-mer. Each plot presents site-by-site coverage (i.e., for each nine-mer) of an M-group natural-sequence alignment by a single tri-valent vaccine candidate. Bars along the x-axis represent the proportion of sequences matched by the vaccine candidate for a given alignment position: 9/9 matches (in red), 8/9 (yellow), 7/9 (blue). Aligned 9-mers are sorted along the x-axis by exact-match coverage value. 656 positions include both the complete Gag and the central region of Nef. For each alignment position, the maximum possible matching value (i.e. the proportion of aligned sequences without gaps in that nine-mer) is shown in gray. FIG. 4A) Non-optimal natural sequences selected from among strains being used in vaccine studies (Kong et al, J. Virol. 77:12764-72 (2003)) including an individual clade A, B, and C viral sequences (Gag: GenBank accession numbers AF004885, K03455, and U52953; Nef core: AF069670, K02083, and U52953). FIG. 4B) Optimum set of natural sequences [isolates US2 (subtype B, USA), 70177 (subtype C, India), and 99TH.R2399 (subtype CRF15_01B, Thailand); accession numbers AY173953, AF533131, and_AF530576] selected by choosing the single sequence with maximum coverage, followed by the sequence that had the best coverage when combined with the first (i.e. the best complement), and so on, selected for M group coverage FIG. 4C) Consensus sequence cocktail (M group, B- and C-subtypes). FIG. 4D) 3 mosaic sequences, FIG. 4E) 4 mosaic sequences, FIG. 4F) 6 mosaic sequences. FIGS. 4D-4F were all optimized for M group coverage.

FIGS. 5A and 5B. Overall coverage of vaccine candidates: coverage of 9-mers in C clade sequences using different input data sets for mosaic optimization, allowing different numbers of antigens, and comparing to different candidate vaccines. Exact (blue), 8/9 (one-off; red), and 7/9 (two-off; yellow) coverage was computed for mono- and polyvalent vaccine candidates for Gag (FIG. 5A) and Nef (core) (FIG. 5B) for four test situations: within-clade (C-clade-optimized candidates scored for C-clade coverage), between-clade (B-clade-optimized candidates scored for C-clade coverage), global-against-single-subtype (M-group-optimized candidates scored for C-clade coverage), global-against-global (M-group-optimized candidates scored for global coverage). Within each set of results, vaccine candidates are grouped by number of sequences in the cocktail (1-6); mosaic sequences are plotted with darker colors. “Non-opt” refers to one set of sequences moving into vaccine trials (Kong et al, J. Virol. 77:12764-72 (2003)); “mosaic” denotes sequences generated by the genetic algorithm; “opt. natural” denotes intact natural sequences selected for maximum 9-mer coverage; “MBC consensus” denotes a cocktail of 3 consensus sequences, for M-group, B-subtype, and C-subtype. For ease of comparison, a dashed line marks the coverage of a 4-sequence set of M-group mosaics (73.7-75.6%). Over 150 combinations of mosaic-number, virus subset, protein region, and optimization and test sets were tested. The C clade/B clade/M group comparisons illustrated in this figure are generally representative of within-clade, between-clade, and M group coverage. In particular, levels of mosaic coverage for B and C clade were very similar, despite there being many more C clade sequences in the Gag collection, and many more B clade sequences in the Nef collection (see FIG. 6 for a full B and C clade comparison). There were relatively few A and G clade sequences in the alignments (24 Gag, 75 Nef), and while 9-mer coverage by M-group optimized mosaics was not as high as for subtypes for B and C clades (4-mosaic coverage for A and G subtypes was 63% for Gag, 74% for Nef), it was much better than a non-optimal cocktail (52% Gag, 52% for Nef).

FIGS. 6A and 6B. Overall coverage of vaccine candidates: coverage of 9-mers in B-clade, C-clade, and M-group sequences using different input data sets for mosaic optimization, allowing different numbers of antigens, and comparing to different candidate vaccines. Exact (blue), 8/9 (one-off; red), and 7/9 (two-off; yellow) coverage was computed for mono- and polyvalent vaccine candidates for Gag (FIG. 6A) and Nef (core) (FIG. 6B) for seven test situations: within-clade (B- or C-clade-optimized candidates scored against the same clade), between-clade (B- or C-clade-optimized candidates scored against the other clade), global vaccine against single subtype (M-group-optimized candidates scored against B- or C-clade), global vaccine against global viruses (M-group-optimized candidates scored against all M-group sequences). Within each set of results, vaccine candidates are grouped by number of sequences in the cocktail (1-6); mosaic sequences are plotted with darker colors. “Non-opt” refers to a particular set of natural sequences previously proposed for a vaccine (Kong, W. P. et al. J Virol 77, 12764-72 (2003)); “mosaic” denotes sequences generated by the genetic algorithm; “opt. natural” denotes intact natural sequences selected for maximum 9-mer coverage; “MBC consensus” denotes a cocktail of 3 consensus sequences, for M-group, B-subtype, and C-subtype. A dashed line is shown at the level of exact-match M-group coverage for a 4-valent mosaic set optimized on the M-group.

FIGS. 7A and 7B. The distribution of 9-mers by frequency of occurrence in natural, consensus, and mosaic sequences. Occurrence counts (y-axis) for different 9-mer frequencies (x-axis) for vaccine cocktails produced by several methods. FIG. 7A: frequencies from 0-60% (for 9-mer frequencies >60%, the distributions are equivalent for all methods). FIG. 7B: Details of low-frequency 9-mers. Natural sequences have large numbers of rare or unique-to-isolate 9-mers (bottom right, FIGS. 7A and 7B); these are unlikely to induce useful vaccine responses. Selecting optimal natural sequences does select for more common 9-mers, but rare and unique 9-mers are still included (top right, FIGS. 7A and 7B). Consensus cocktails, in contrast, under-represent uncommon 9-mers, especially below 20% frequency (bottom left, FIGS. 7A and 7B). For mosaic sequences, the number of lower-frequency 9-mers monotonically increases with the number of sequences (top left, each panel), but unique-to-isolate 9-mers are completely excluded (top left of right panel: * marks the absence of 9-mers with frequencies <0.005).

FIGS. 8A-8D. HLA binding potential of vaccine candidates. FIGS. 8A and 8B) HLA binding motif counts. FIGS. 8C and 8D) number of unfavorable amino acids. In all graphs: natural sequences are marked with black circles (●); consensus sequences with blue triangles (▴); inferred ancestral sequences with green squares (

); and mosaic sequences with red diamonds (♦). Left panel (FIGS. 8A and 8C) shows HLA-binding-motif counts (FIG. 8A) and counts of unfavorable amino acids (FIG. 8C) calculated for individual sequences; Right panel (FIGS. 8B and 8D) shows HLA binding motifs counts (FIG. 8B) and counts of unfavorable amino acids (FIG. 8D) calculated for sequence cocktails. The top portion of each graph (box-and-whiskers graph) shows the distribution of respective counts (motif counts or counts of unfavorable amino acids) based either on alignment of M group sequences (for individual sequences, FIGS. 8A and 8C) or on 100 randomly composed cocktails of three sequences, one from each A, B and C subtypes (for sequence cocktails, FIGS. 8B and 8D). The alignment was downloaded from the Los Alamos HIV database. The box extends from the 25 percentile to the 75 percentile, with the line at the median. The whiskers extending outside the box show the highest and lowest values. Amino acids that are very rarely found as C-terminal anchor residues are G, S, T, P, N, Q, D, E, and H, and tend to be small, polar, or negatively charged (Yusim et al, J. Virol. 76:8757-8768 (2002)). Results are shown for Gag, but the same qualitative results hold for Nef core and complete Nef. The same procedure was done for supertype motifs with results qualitatively similar to the results for HLA binding motifs (data not shown).

FIG. 9. Mosaic protein sets limited to 4 sequences (k=4), spanning Gag and the central region of Nef, optimized for subtype B, subtype C, and the M group. Figure discloses SEQ ID NOs: 1-84, respectively, in order of appearance.

FIG. 10. Mosaic sets for Env and Pol. Figure discloses SEQ ID NOs: 85-168, respectively, in order of appearance.

DETAILED DESCRIPTION OF THE INVENTION

The present invention results from the realization that a polyvalent set of antigens comprising synthetic viral proteins, the sequences of which provide maximum coverage of non-rare short stretches of circulating viral sequences, constitutes a good vaccine candidate. The invention provides a “genetic algorithm” strategy to create such sets of polyvalent antigens as mosaic blends of fragments of an arbitrary set of natural protein sequences provided as inputs. In the context of HIV, the proteins Gag and the inner core (but not the whole) of Nef are ideal candidates for such antigens. The invention further provides optimized sets for these proteins.

The genetic algorithm strategy of the invention uses unaligned protein sequences from the general population as an input data set, and thus has the virtue of being “alignment independent”. It creates artificial mosaic proteins that resemble proteins found in nature—the success of the consensus antigens in small animals models suggest this works well. 9 Mers are the focus of the studies described herein, however, different length peptides can be selected depending on the intended target. In accordance with the present approach, 9 mers (for example) that do not exist in nature or that are very rare can be excluded—this is an improvement relative to consensus sequences since the latter can contain some 9 mers (for example) that have not been found in nature, and relative to natural strains that almost invariably contain some 9 mers (for example) that are unique to that strain. The definition of fitness used for the genetic algorithm is that the most “fit” polyvalent cocktail is the combination of mosaic strains that gives the best coverage (highest fraction of perfect matches) of all of the 9 mers in the population and is subject to the constraint that no 9 mer is absent or rare in the population.

The mosaics protein sets of the invention can be optimized with respect to different input data sets—this allows use of current data to assess virtues of a subtype or region specific vaccines from a T cell perspective. By way of example, options that have been compared include:

-   -   1) Optimal polyvalent mosaic sets based on M group, B clade and         C clade. The question presented was how much better is         intra-clade coverage than inter-clade or global.     -   2) Different numbers of antigens: 1, 3, 4, 6     -   3) Natural strains currently in use for vaccine protocols just         to exemplify “typical” strains (Merck, VRC)     -   4) Natural strains selected to give the best coverage of 9-mers         in a population     -   5) Sets of consensus: A+B+C.     -   6) Optimized cocktails that include one “given” strain in a         polyvalent antigen, one ancestral+3 mosaic strains, one         consensus+3 mosaic strains.     -   7) Coverage of 9 mers that were perfectly matched was compared         with those that match 8/9, 7/9, and 6/9 or less.         This is a computationally difficult problem, as the best set to         cover one 9-mer may not be the best set to cover overlapping         9-mers.

It will be appreciated from a reading of this disclosure that the approach described herein can be used to design peptide reagents to test HIV immune responses, and be applied to other variable pathogens as well. For example, the present approach can be adapted to the highly variable virus Hepatitis C.

The proteins/polypeptides/peptides (“immunogens”) of the invention can be formulated into compositions with a pharmaceutically acceptable carrier and/or adjuvant using techniques well known in the art. Suitable routes of administration include systemic (e.g. intramuscular or subcutaneous), oral, intravaginal, intrarectal and intranasal.

The immunogens of the invention can be chemically synthesized and purified using methods which are well known to the ordinarily skilled artisan. The immunogens can also be synthesized by well-known recombinant DNA techniques.

Nucleic acids encoding the immunogens of the invention can be used as components of, for example, a DNA vaccine wherein the encoding sequence is administered as naked DNA or, for example, a minigene encoding the immunogen can be present in a viral vector. The encoding sequences can be expressed, for example, in mycobacterium, in a recombinant chimeric adenovirus, or in a recombinant attenuated vesicular stomatitis virus. The encoding sequence can also be present, for example, in a replicating or non-replicating adenoviral vector, an adeno-associated virus vector, an attenuated mycobacterium tuberculosis vector, a Bacillus Calmette Guerin (BCO) vector, a vaccinia or Modified Vaccinia Ankara (MVA) vector, another pox virus vector, recombinant polio and other enteric virus vector, Salmonella species bacterial vector, Shigella species bacterial vector, Venezuelean Equine Encephalitis Virus (VEE) vector, a Semliki Forest Virus vector, or a Tobacco Mosaic Virus vector. The encoding sequence, can also be expressed as a DNA plasmid with, for example, an active promoter such as a CMV promoter. Other live vectors can also be used to express the sequences of the invention. Expression of the immunogen of the invention can be induced in a patient's own cells, by introduction into those cells of nucleic acids that encode the immunogen, preferably using codons and promoters that optimize expression in human cells. Examples of methods of making and using DNA vaccines are disclosed in U.S. Pat. Nos. 5,580,859, 5,589,466, and 5,703,055.

It will be appreciated that adjuvants can be included in the compositions of the invention (or otherwise administered to enhance the immunogenic effect). Examples of suitable adjuvants include TRL-9 agonists, TRL-4 agonists, and TRL-7, 8 and 9 agonist combinations (as well as alum). Adjuvants can take the form of oil and water emulsions. Squalene adjuvants can also be used.

The composition of the invention comprises an immunologically effective amount of the immunogen of this invention, or nucleic acid sequence encoding same, in a pharmaceutically acceptable delivery system. The compositions can be used for prevention and/or treatment of virus infection (e.g. HIV infection). As indicated above, the compositions of the invention can be formulated using adjuvants, emulsifiers, pharmaceutically-acceptable carriers or other ingredients routinely provided in vaccine compositions. Optimum formulations can be readily designed by one of ordinary skill in the art and can include formulations for immediate release and/or for sustained release, and for induction of systemic immunity and/or induction of localized mucosal immunity (e.g, the formulation can be designed for intranasal, intravaginal or intrarectal administration). As noted above, the present compositions can be administered by any convenient route including subcutaneous, intranasal, oral, intramuscular, or other parenteral or enteral route. The immunogens can be administered as a single dose or multiple doses. Optimum immunization schedules can be readily determined by the ordinarily skilled artisan and can vary with the patient, the composition and the effect sought.

The invention contemplates the direct use of both the immunogen of the invention and/or nucleic acids encoding same and/or the immunogen expressed as indicated above. For example, a minigene encoding the immunogen can be used as a prime and/or boost.

The invention includes any and all amino acid sequences disclosed herein, as well as nucleic acid sequences encoding same (and nucleic acids complementary to such encoding sequences).

Specifically disclosed herein are vaccine antigen sets optimized for single B or C subtypes, targeting regional epidemics, as well as for all HIV-1 variants in global circulation [the HIV-1 Main (M) group]. In the study described in the Example that follows, the focus is on designing polyvalent vaccines specifically for T-cell responses. HIV-1 specific T-cells are likely to be crucial to an HIV-1-specific vaccine response: CTL responses are correlated with slow disease progression in humans (Oxenius et al, J. Infect. Dis. 189:1199-1208 (2004)), and the importance of CTL responses in nonhuman primate vaccination models is well-established. Vaccine elicited cellular immune responses help control pathogenic SIV or SHIV, and reduce the likelihood of disease after challenge with pathogenic virus (Barouch et al, Science 290:486-492 (2000)). Temporary depletion of CD8+ T cells results in increased viremia in SIV-infected rhesus macaques (Schmitz et al, Science 283:857-860 (1999)). Furthermore, the evolution of escape mutations has been associated with disease progression, indicating that CTL responses help constrain viral replication in vivo (Barouch et al, J. Virol. 77:7367-7375 (2003)), and so vaccine-stimulated memory responses that could block potential escape routes may be of value. While the highly variable Envelope (Env) is the primary target for neutralizing antibodies against HIV, and vaccine antigens will also need to be tailored to elicit these antibody responses (Moore & Burton, Nat. Med. 10:769-771 (2004)), T-cell vaccine components can target more conserved proteins to trigger responses that are more likely to cross-react. But even the most conserved HIV-1 proteins are diverse enough that variation will be an issue. Artificial central-sequence vaccine approaches, consensus and ancestral sequences (Gaschen et al, Science 296:2354-2360 (2002), Gao et al, J. Virol. 79:1154-1163 (2005), Doria-Rose et al, J. Virol. 79:11214-11224 (2005)), which essentially “split the differences” between strains, show promise, stimulating responses with enhanced cross-reactivity compared to natural strain vaccines (Gao et al, J. Virol. 79:1154-1163 (2005)) (Liao et al. and Weaver et al., submitted.) Nevertheless, even central strains cover the spectrum of HIV diversity to a very limited extent, and consensus-based peptide reagents fail to detect many autologous CD8+ T-cell responses (Altfeld et al, J. Virol. 77:7330-7340 (2003)).

A single amino acid substitution can mediate T-cell escape, and as one or more amino acids in many T-cell epitopes differ between HIV-1 strains, the potential effectiveness of responses to any one vaccine antigen is limited. Whether a particular mutation will diminish T-cell cross-reactivity is epitope- and T-cell-specific, although some changes can broadly affect between-clade cross-reactivity (Norris et al, AIDS Res. Hum. Retroviruses 20:315-325 (2004)). Including more variants in a polyvalent vaccine could enable responses to a broader range of circulating variants. It could also prime the immune system against common escape variants (Jones et al, J. Exp. Med. 200:1243-1256 (2004)); escape from one T-cell receptor might create a variant that is susceptible to another (Lee et al, J. Exp. Med. 200:1455-1466 (2004)), thus stimulating polyclonal responses to epitope variants may be beneficial (Killian et al, AIDS 19:887-896 (2005)). Immune escape involving avenues that inhibit processing (Milicic et al, J. Immunol. 175:4618-4626 (2005)) or HLA binding (Ammaranond et al, AIDS Res. Hum. Retroviruses 21:395-397 (2005)) prevent epitope presentation, and in such cases the escape variant could not be countered by a T-cell with a different specificity. However, it is possible the presence of T-cells that recognize overlapping epitopes may in some cases block these even escape routes.

Certain aspects of the invention can be described in greater detail in the non-limiting Example that follows.

Example Experimental Details

HIV-1 Sequence Data.

The reference alignments from the 2005 HIV sequence database (URL: hiv-dot-lanl-dot-gov), which contain one sequence per person, were used, supplemented by additional recently available C subtype Gag and Nef sequences from Durban, South Africa (GenBank accession numbers AY856956-AY857186) (Kiepiela et al, Nature 432:769-75 (2004)). This set contained 551 Gag and 1,131 Nef M group sequences from throughout the globe; recombinant sequences were included as well as pure subtype sequences for exploring M group diversity. The subsets of these alignments that contained 18 A, 102 B, 228 C, and 6 G subtype (Gag), and 62 A, 454 B, 284 C, and 13 G subtype sequences (Nef) sequences were used for within- and between-single-clade optimizations and comparisons.

The Genetic Algorithm.

GAs are computational analogues of biological processes (evolution, populations, selection, recombination) used to find solutions to problems that are difficult to solve analytically (Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, (M.I.T. Press, Cambridge, Mass. (1992))). Solutions for a given input are “evolved” though a process of random modification and selection according to a “fitness” (optimality) criterion. GAs come in many flavors; a “steady-state co-evolutionary multi-population” GA was implemented. “Steady-state” refers to generating one new candidate solution at a time, rather than a whole new population at once; and “co-evolutionary” refers to simultaneously evolving several distinct populations that work together to form a complete solution. The input is an unaligned set of natural sequences; a candidate solution is a set of k pseudo-natural “mosaic” sequences, each of which is formed by concatenating sections of natural sequences. The fitness criterion is population coverage, defined as the proportion of all 9-amino-acid sequence fragments (potential epitopes) in the input sequences that are found in the cocktail.

To initialize the GA (FIG. 2), k populations of n initial candidate sequences are generated by 2-point recombination between randomly selected natural sequences. Because the input natural sequences are not aligned, “homologous” crossover is used: crossover points in each sequence are selected by searching for short matching strings in both sequences; strings of c−1=8, were used where a typical epitope length is c=9. This ensures that the recombined sequences resemble natural proteins: the boundaries between sections of sequence derived from different strains are seamless, the local sequences spanning the boundaries are always found in nature, and the mosaics are prevented from acquiring large insertions/deletions or unnatural combinations of amino acids. Mosaic sequence lengths fall within the distribution of natural sequence lengths as a consequence of mosaic construction: recombination is only allowed at identical regions, reinforced by an explicit software prohibition against excessive lengths to prevent reduplication of repeat regions. (Such “in frame” insertion of reduplicated epitopes could provide another way of increasing coverage without generating unnatural 9-mers, but their inclusion would create “unnatural” proteins.) Initially, the cocktail contains one randomly chosen “winner” from each population. The fitness score for any individual sequence in a population is the coverage value for the cocktail consisting of that sequence plus the current winners from the other populations. The individual fitness of any sequence in a population therefore depends dynamically upon the best sequences found in the other populations.

Optimization proceeds one population at a time. For each iteration, two “parent” sequences are chosen. The first parent is chosen using “2-tournament” selection: two sequences are picked at random from the current population, scored, and the better one is chosen. This selects parents with a probability inversely proportional to their fitness rank within the population, without the need to actually compute the fitness of all individuals. The second parent is chosen in the same way (50% of the time), or is selected at random from the set of natural sequences. 2-point homologous crossover between the parents is then used to generate a “child” sequence. Any child containing a 9-mer that was very rare in the natural population (found less than 3 times) is rejected immediately. Otherwise, the new sequence is scored, and its fitness is compared with the fitnesses of four randomly chosen sequences from the same population. If any of the four randomly chosen sequences has a score lower than that of the new sequence, it is replaced in the population by the new sequence. Whenever a sequence is encountered that yields a better score than the current population “winner”, that sequence becomes the winner for the current population and so is subsequently used in the cocktail to evaluate sequences in other populations. A few such optimization cycles (typically 10) are applied to each population in turn, and this process continues cycling through the populations until evolution stalls (i.e., no improvement has been made for a defined number of generations). At this point, the entire procedure is restarted using newly generated random starting populations, and the restarts are continued until no further improvement is seen. The GA was run on each data set with n=50 or 500; each run was continued until no further improvement occurred for 12-24 hours on a 2 GHz Pentium processor. Cocktails were generated having k=1, 3, 4, or 6 mosaic sequences.

The GA also enables optional inclusion of one or more fixed sequences of interest (for example, a consensus) in the cocktail and will evolve the other elements of the cocktail in order to optimally complement that fixed strain. As these solutions were suboptimal, they are not included here. An additional program selects from the input file the k best natural strains that in combination provide the best population coverage.

Comparison with Other Polyvalent Vaccine Candidates.

Population coverage scores were computed for other potential mono- or polyvalent vaccines to make direct comparisons with the mosaic-sequence vaccines, tracking identities with population 9-mers, as well as similarities of 8/9 and 7/9 amino acids. Potential vaccine candidates based on natural strains include single strains (for example, a single C strain for a vaccine for southern Africa (Williamson et al, AIDS Res. Hum. Retroviruses 19:133-44 (2003))) or combinations of natural strains (for example, one each of subtype A, B, and C (Kong et al, J. Virol. 77:12764-72 (2003)). To date, natural-strain vaccine candidates have not been systematically selected to maximize potential T-cell epitope coverage; vaccine candidates were picked from the literature to be representative of what could be expected from unselected vaccine candidates. An upper bound for coverage was also determined using only intact natural strains: optimal natural-sequence cocktails were generated by selecting the single sequence with the best coverage of the dataset, and then successively adding the most complementary sequences up to a given k. The comparisons included optimal natural-sequence cocktails of various sizes, as well as consensus sequences, alone or in combination (Gaschen et al, Science 296:2354-60 (2002)), to represent the concept of central, synthetic vaccines. Finally, using the fixed-sequence option in the GA, consensus-plus-mosaic combinations in the comparisons; these scores were essentially equivalent to all-mosaic combinations were included for a given k (data not shown). The code used for performing these analyses are available at: ftp://ftp-t10/pub/btk/mosaics.

Results

Protein Variation.

In conserved HIV-1 proteins, most positions are essentially invariant, and most variable positions have only two to three amino acids that occur at appreciable frequencies, and variable positions are generally well dispersed between conserved positions. Therefore, within the boundaries of a CD8+ T-cell epitope (8-12 amino acids, typically nine), most of the population diversity can be covered with very few variants. FIG. 1 shows an upper bound for population coverage of 9-mers (stretches of nine contiguous amino acids) comparing Gag, Nef, and Env for increasing numbers of variants, sequentially adding variants that provide the best coverage. In conserved regions, a high degree of population coverage is achieved with 2-4 variants. By contrast, in variable regions like Env, limited population coverage is possible even with eight, variants. Since each new addition is rarer, the relative benefits of each addition diminish as the number of variants increases.

Vaccine Design Optimization Strategies.

FIG. 1 shows an idealized level of 9-mer coverage. In reality, high-frequency 9-mers often conflict: because of local co-variation, the optimal amino acid for one 9-mer may differ from that for an overlapping 9-mer. To design mosaic protein sets that optimize population coverage, the relative benefits of each amino acid must be evaluated in combination with nearby variants. For example, Alanine (Ala) and Glutamate (Glu) might each frequently occur in adjacent positions, but if the Ala-Glu combination is never observed in nature, it should be excluded from the vaccine. Several optimization strategies were investigated: a greedy algorithm, a semi-automated compatible-9mer assembly strategy, an alignment-based genetic algorithm (GA), and an alignment-independent GA.

The alignment-independent GA generated mosaics with the best population coverage. This GA generates a user-specified number of mosaic sequences from a set of unaligned protein sequences, explicitly excluding rare or unnatural epitope-length fragments (potentially introduced at recombination breakpoints) that could induce non-protective vaccine-antigen-specific responses. These candidate vaccine sequences resemble natural proteins, but are assembled from frequency-weighted fragments of database sequences recombined at homologous breakpoints (FIG. 2); they approach maximal coverage of 9-mers for the input population.

Selecting HIV Protein Regions for an Initial Mosaic Vaccine.

The initial design focused on protein regions meeting specific criteria: i) relatively low variability, ii) high levels of recognition in natural infection, iii) a high density of known epitopes and iv) either early responses upon infection or CD8+ T-cell responses associated with good outcomes in infected patients. First, an assessment was made of the level of 9-mer coverage achieved by mosaics for different HIV proteins (FIG. 3). For each protein, a set of four mosaics was generated using either the M group or the B- and C-subtypes alone; coverage was scored on the C subtype. Several results are notable: i) within-subtype optimization provides the best within-subtype coverage, but substantially poorer between-subtype coverage—nevertheless, B-subtype-optimized mosaics provide better C-subtype coverage than a single natural B subtype protein (Kong et al, J. Virol. 77:12764-72 (2003)); ii) Pol and Gag have the most potential to elicit broadly cross-reactive responses, whereas Rev, Tat, and Vpu have even fewer conserved 9-mers than the highly variable Env protein, iii) within-subtype coverage of M-group-optimized mosaic sets approached coverage of within-subtype optimized sets, particularly for more conserved proteins.

Gag and the central region of Nef meet the four criteria listed above. Nef is the HIV protein most frequently recognized by T-cells (Frahm et al, J. Virol. 78:2187-200 (2004)) and the target for the earliest response in natural infection (Lichterfeld et al, Aids 18:1383-92 (2004)). While overall it is variable (FIG. 3), its central region is as conserved as Gag (FIG. 1). It is not yet clear what optimum proteins for inclusion in a vaccine might be, and mosaics could be designed to maximize the potential coverage of even the most variable proteins (FIG. 3), but the prospects for global coverage are better for conserved proteins. Improved vaccine protection in macaques has been demonstrated by adding Rev, Tat, and Nef to a vaccine containing Gag, Pol, and Env (Hel et al, J. Immunol. 176:85-96 (2006)), but this was in the context of homologous challenge, where variability was not an issue. The extreme variability of regulatory proteins in circulating virus populations may preclude cross-reactive responses; in terms of conservation, Pol, Gag (particularly p24) and the central region of Nef (HXB2 positions 65-149) are promising potential immunogens (FIGS. 1,3). Pol, however, is infrequently recognized during natural infection (Frahm et al, J. Virol. 78:2187-200 (2004)), so it was not included in the initial immunogen design. The conserved portion of Nef that were included contains the most highly recognized peptides in HIV-1 (Frahm et al, J. Virol. 78:2187-200 (2004)), but as a protein fragment, would not allow Nef's immune inhibitory functions (e.g. HLA class I down-regulation (Blagoveshchenskaya, Cell 111:853-66 (2002))). Both Gag and Nef are densely packed with overlapping well-characterized CD8+ and CD4+ T-cell epitopes, presented by many different iHLA molecules (http://www.hiv.lanl.gov//content/immunology/maps/maps.html), and Gag-specific CD8+ (Masemola et al, J. Virol. 78:3233-43 (2004)) and CD4+ (Oxenius et al, J. Infect. Dis. 189:1199-208 (2004)) T-cell responses have been associated with low viral set points in infected individuals (Masemola et al, J. Virol. 78:3233-43 (2004)).

To examine the potential impact of geographic variation and input sample size, a limited test was done using published subtype C sequences. The subtype C Gag data were divided into three sets of comparable size—two South African sets (Kiepiela et al, Nature 432:769-75 (2004)), and one non-South-African subtype C set. Mosaics were optimized independently on each of the sets, and the resulting mosaics were tested against all three sets. The coverage of 9-mers was slightly better for identical training and test sets (77-79% 9/9 coverage), but essentially equivalent when the training and test sets were the two different South African data sets (73-75%), or either of the South African sets and the non-South African C subtype sequences (74-76%). Thus between- and within-country coverage approximated within-clade coverage, and in this case no advantage to a country-specific C subtype mosaic design was found.

Designing Mosaics for Gag and Nef and Comparing Vaccine Strategies.

To evaluate within- and between-subtype cross-reactivity for various vaccine design strategies, a calculation was made of the coverage they provided for natural M-Group sequences. The fraction of all 9-mers in the natural sequences that were perfectly matched by 9-mers in the vaccine antigens were computed, as well as those having 8/9 or 7/9 matching amino acids, since single (and sometimes double) substitutions within epitopes may retain cross-reactivity. FIG. 4 shows M group coverage per 9-mer in Gag and the central region of Nef for cocktails designed by various strategies: a) three non-optimal natural strains from the A, B, and C subtypes that have been used as vaccine antigens (Kong et al, J. Virol. 77:12764-72 (2003)); b) three natural strains that were computationally selected to give the best M group coverage; c) M group, B subtype, and C subtype consensus sequences; and, d,e,f) three, four and six mosaic proteins. For cocktails of multiple strains, sets of k=3, k=4, and k=6, the mosaics clearly perform the best, and coverage approaches the upper bound for k strains. They are followed by optimally selected natural strains, the consensus protein cocktail, and finally, non-optimal natural strains. Allowing more antigens provides greater coverage, but gains for each addition are reduced as k increases (FIGS. 1 and 4).

FIG. 5 summarizes total coverage for the different vaccine design strategies, from single proteins through combinations of mosaic proteins, and compares within-subtype optimization to M group optimization. The performance of a single mosaic is comparable to the best single natural strain or a consensus sequence. Although a single consensus sequence out-performs a single best natural strain, the optimized natural-sequence cocktail does better than the consensus cocktail: the consensus sequences are more similar to each other than are natural strains, and are therefore somewhat redundant. Including even just two mosaic variants, however, markedly increases coverage, and four and six mosaic proteins give progressively better coverage than polyvalent cocktails of natural or consensus strains. Within-subtype optimized mosaics perform best—with four mosaic antigens 80-85% of the 9-mers are perfectly matched—but between-subtype coverage of these sets falls off dramatically, to 50-60%. In contrast, mosaic proteins optimized using the full M group give coverage of approximately 75-80% for individual subtypes, comparable to the coverage of the M group as a whole (FIGS. 5 and 6). If imperfect 8/9 matches are allowed, both M group optimized and within-subtype optimized mosaics approach 90% coverage.

Since coverage is increased by adding progressively rarer 9-mers, and rare epitopes may be problematic (e.g., by inducing vaccine-specific immunodominant responses), an investigation was made of the frequency distribution of 9-mers in the vaccine constructs relative to the natural sequences from which they were generated. Most additional epitopes in a k=6 cocktail compared to a k=4 cocktail are low-frequency (<0.1, FIG. 7). Despite enhancing coverage, these epitopes are relatively rare, and thus responses they induce might draw away from vaccine responses to more common, thus more useful, epitopes. Natural-sequence cocktails actually have fewer occurrences of moderately low-frequency epitopes than mosaics, which accrue some lower frequency 9-mers as coverage is optimized. On the other hand, the mosaics exclude unique or very rare 9-mers, while natural strains generally contain 9-mers present in no other sequence. For example, natural M group Gag sequences had a median of 35 (range 0-148) unique 9-mers per sequence. Retention of HLA-anchor motifs was also explored, and anchor motif frequencies were found to be comparable between four mosaics and three natural strains. Natural antigens did exhibit an increase in number of motifs per antigen, possibly due to inclusion of strain-specific motifs (FIG. 8).

The increase in ever-rarer epitopes with increasing k, coupled with concerns about vaccination-point dilution and reagent development costs, resulted in the initial production of mosaic protein sets limited to 4 sequences (k=4), spanning Gag and the central region of Nef, optimized for subtype B, subtype C, and the M group (these sequences are included in FIG. 9; mosaic sets for Env and Pol are set forth in FIG. 10). Synthesis of various four-sequence Gag-Nef mosaics and initial antigenicity studies are underway. In the initial mosaic vaccine, targeted are just Gag and the center of the Nef protein, which are conserved enough to provide excellent global population coverage, and have the desirable properties described above in terms of natural responses (Bansal et al, Aids 19:241-50 (2005)). Additionally, including B subtype p24 variants in Elispot peptide mixtures to detect natural CTL responses to infection significantly enhanced both the number and the magnitude of responses detected supporting the idea that including variants of even the most conserved proteins will be useful. Finally, cocktails of proteins in a polyvalent HIV-1 vaccine given to rhesus macaques did not interfere with the development of robust responses to each antigen (Seaman et al, J. Virol. 79:2956-63 (2005)), and antigen cocktails did not produce antagonistic responses in murine models (Singh et al, J. Immunol. 169:6779-86 (2002)), indicating that antigenic mixtures are appropriate for T-cell vaccines.

Even with mosaics, variable proteins like Env have limited coverage of 9-mers, although mosaics improve coverage relative to natural strains. For example three M group natural proteins, one each selected from the A, B, and C clades, and currently under study for vaccine design (Seaman et al, J. Virol. 79:2956-63 (2005)) perfectly match only 39% of the 9-mers in M group proteins, and 65% have at least 8/9 matches. In contrast, three M group Env mosaics match 47% of 9-mers perfectly, and 70% have at least an 8/9 match. The code written to design polyvalent mosaic antigens is available, and could readily be applied to any input set of variable proteins, optimized for any desired number of antigens. The code also allows selection of optimal combinations of k natural strains, enabling rational selection of natural antigens for polyvalent vaccines. Included in Table 1 are the best natural strains for Gag and Nef population coverage of current database alignments.

TABLE 1 Natural sequence cocktails having the best available 9-mer coverage for different genes, subtype sets, and numbers of sequences Gag, B-subtype, 1 natural sequence B.US.86.AD87_AF004394 Gag, B-subtype, 3 natural sequences B.US.86.AD87_AF004394 B.US.97.Ac_06_AY247251 B.US.88.WR27_AF286365 Gag, B-subtype, 4 natural sequences B.US.86.AD87_AF004394 B.US.97.Ac_06_AY247251 B.US._.R3_PDC1_AY206652 B.US.88.WR27_AF286365 Gag, B-subtype, 6 natural sequences B.CN._.CNHN24_AY180905 B.US.86.AD87_AF004394 B.US.97.Ac_06_AY247251 B.US._.P2_AY206654 B.US._.R3_PDC1_AY206652 B.US.88.WR27_AF286365 Gag, C-subtype, 1 natural sequence C.IN._.70177_AF533131 Gag, C-subtype, 3 natural sequences C.ZA.97.97ZA012 C.ZA.x.04ZASK161B1 C.IN.^(—).70177_AF533131 Gag, C-subtype, 4 natural sequences C.ZA.97.97ZA012 C.ZA.x.04ZASK142B1 C.ZA.x.04ZASK161B1 C.IN._.70177_AF533131 Gag, C-subtype, 6 natural sequences C.ZA.97.97ZA012 C.ZA.x.04ZASK142B1 C.ZA.x.04ZASK161B1 C.BW.99.99BWMC168_AF443087 C.IN._.70177_AF533131 C.IN._.MYA1_AF533139 Gag, M-group, 1 natural sequence C.IN._.70177_AF533131 Gag, M-group, 3 natural sequences B.US.90.US2_AY173953 C.IN.-.70177_AF533131 15_01B.TH.99.99TH_R2399_AF530576 Gag, M-group, 4 natural sequences B.US.90.US2_AY173953 C.IN._.70177_AF533131 C.IN.93.93IN999_AF067154 15_01B.TH.99.99TH_R2399_AF530576 Gag, M-group, 6 natural sequences C.ZA.x.04ZASK138B1 B.US.90.US2_AY173953 B.US._.WT1_PDC1_AY206656 C.IN._.70177_AF533131 C.IN.93.93IN999_AF067154 15_01B.TH.99.99TH_R2399_AF530576 Nef (central region), B-subtype, 1 natural sequence B.GB.94.028jh_94_1_NP_AF129346 Nef (central region), B-subtype, 3 natural sequences B.GB.94.028jh_94_1_NP_AF129346 B.KR.96.96KCS4_AY121471 B.FR.83.HXB2_K03455 Nef (central region), B-subtype, 4 natural sequences B.GB.94.028jh_94_1_NP_AF129346 B.KR.96.96KCS4_AY121471 B.US.90.E90NEF_U43108 B.FR.83.HXB2_K03455 Nef (central region), B-subtype, 6 natural sequences B.GB.94.028jh_94_1_NP_AF129346 B.KR.02.02HYJ3_AY121454 B.KR.96.96KCS4_AY121471 B.CN._.RL42_U71182 B.US.90.E90NEF_U43108 B.FR.83.HXB2_K03455 Nef (central region), C-subtype, 1 natural sequence C.ZA.04.04ZASK139B1 Nef (central region), C-subtype, 3 natural sequences C.ZA.04.04ZASK180B1 C.ZA.04.04ZASK139B1 C.ZA._.ZASW15_AF397568 Nef (central region), C-subtype, 4 natural sequences C.ZA.97.ZA97004_AF529682 C.ZA.04.04ZASK180B1 C.ZA.04.04ZASK139B1 C.ZA._.ZASW15_AF397568 Nef (central region), C-subtype, 6 natural sequences C.ZA.97.ZA97004_AF529682 C.ZA.00.1192M3M C.ZA.04.04ZASK180B1 C.ZA.04.04ZASK139B1 C.04ZASK184B1 C.ZA._.ZASW15_AF397568 Nef (central region), M-group, 1 natural sequence B.GB.94.028jh_94_1_NP_AF129346 Nef (central region), M-group, 3 natural sequences 02_AG.CM._.98CM1390_AY265107 C.ZA.03.03ZASK020B2 B.GB.94.028jh_94_1_NP_AF129346 Nef (central region), M-group, 4 natural sequences 02_AG.CM._.98CM1390_AY265107 01A1.MM.99.mCSW105_AB097872 C.ZA.03.03ZASK020B2 B.GB.94.028jh_94_1_NP_AF129346 Nef (central region), M-group, 6 natural sequences 02_AG.CM._.98CM1390_AY265107 01A1.MM.99.mCSW105_AB097872 C.ZA.03.03ZASK020B2 C.03ZASK111B1 B.GB.94.028jh_94_1_NP_AF129346 B.KR.01.01CWS2_AF462757

Summarizing, the above-described study focuses on the design of T-cell vaccine components to counter HIV diversity at the moment of infection, and to block viral escape routes and thereby minimize disease progression in infected individuals. The polyvalent mosaic protein strategy developed here for HIV-1 vaccine design could be applied to any variable protein, to other pathogens, and to other immunological problems. For example, incorporating a minimal number of variant peptides into T-cell response assays could markedly increase sensitivity without excessive cost: a set of k mosaic proteins provides the maximum coverage possible for k antigens.

A centralized (consensus or ancestral) gene and protein strategy has been proposed previously to address HIV diversity (Gaschen et al, Science 296:2354-2360 (2002)). Proof-of-concept for the use of artificial genes as immunogens has been demonstrated by the induction of both T and B cell responses to wild-type HIV-1 strains by group M consensus immunogens (Gaschen et al, Science 296:2354-2360 (2002), Gao et al, J. Virol. 79:1154-63 (2005), Doria-Rose et al, J. Virol. 79:11214-24 (2005), Weaver et al, J. Virol., in press)). The mosaic protein design improves on consensus or natural immunogen design by co-optimizing reagents for a polyclonal vaccine, excluding rare CD8+ T-cell epitopes, and incorporating variants that, by virtue of their frequency at the population level, are likely to be involved in escape pathways.

The mosaic antigens maximize the number of epitope-length variants that are present in a small, practical number of vaccine antigens. The decision was made to use multiple antigens that resemble native proteins, rather than linking sets of concatenated epitopes in a poly-epitope pseudo-protein (Hanke et al, Vaccine 16:426-35 (1998)), reasoning that in vivo processing of native-like vaccine antigens will more closely resemble processing in natural infection, and will also allow expanded coverage of overlapping epitopes. T-cell mosaic antigens would be best employed in the context of a strong polyvalent immune response; improvements in other areas of vaccine design and a combination of the best strategies, incorporating mosaic antigens to cover diversity, may ultimately enable an effective cross-reactive vaccine-induced immune response against HIV-1.

All documents and other information sources cited above are hereby incorporated in their entirety by reference. 

What is claimed is:
 1. A nucleic acid comprising nucleotides encoding a polypeptide selected from the group consisting of SEQ ID NO: 72 (Gag M syn3.1), SEQ ID NO: 73 (Gag M syn3.2) and SEQ ID NO: 74 (Gag M syn3.3).
 2. The nucleic acid of claim 1 encoding the polypeptide of SEQ ID NO: 72 (Gag M syn3.1).
 3. The nucleic acid of claim 1 encoding the polypeptide of SEQ ID NO: 73 (Gag M syn3.2).
 4. The nucleic acid of claim 1 encoding the polypeptide of SEQ ID NO: 74 (Gag M syn3.3).
 5. A vector comprising a nucleic acid of claim 1, wherein the nucleic acid is operably linked to a promoter.
 6. The vector of claim 5, wherein the vector is a viral vector.
 7. The vector of claim 6, wherein the viral vector is an adenoviral vector, an adeno-associated virus vector, a pox virus vector, an enteric virus vector, a Venezuelean Equine Encephalitis Virus vector, a Semliki Forest Virus vector, or a Tobacco Mosaic Virus vector.
 8. A composition comprising at least one of the nucleic acids of claim 1 and a carrier.
 9. A composition comprising the vector of claim 5 and a carrier.
 10. The composition of claim 8, wherein the composition further comprises an adjuvant.
 11. The composition of claim 9, wherein the composition further comprises an adjuvant.
 12. A trivalent composition comprising nucleic acids comprising nucleotides encoding a polypeptide of SEQ ID NO: 72 (Gag M syn3.1), SEQ ID NO: 73 (Gag M syn3.2), and SEQ ID NO: 74 (Gag M syn3.3).
 13. The trivalent composition of claim 12, wherein the composition comprises nucleic acids encoding the polypeptide of SEQ ID NO: 72 (Gag M syn3.1), SEQ ID NO: 73 (Gag M syn3.2) and SEQ ID NO: 74 (Gag M syn3.3).
 14. The trivalent composition of claim 12 or 13, wherein each nucleic acid is operably linked to a promoter in a vector.
 15. The trivalent composition of claim 14, wherein the vector is a viral vector.
 16. The trivalent composition of claim 15, wherein the viral vectors is an adenoviral vector, an adeno-associated virus vector, a pox virus vector, an enteric virus vector, a Venezuelean Equine Encephalitis Virus vector, a Semliki Forest Virus vector, or a Tobacco Mosaic Virus vector.
 17. The trivalent composition of claim 12 or 13 further comprising an adjuvant.
 18. A method of inducing an immune response against HIV-1 in a host comprising administering to the host the composition of claim 12 or 13 in an amount sufficient to induce the response. 